Abstract
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.
Abstract (translated)
记忆已成为基于基础模型的代理的核心能力，并且将继续保持这一地位。随着关于代理记忆的研究迅速扩展并吸引前所未有的关注，该领域也变得越来越碎片化。现有属于代理记忆范畴的工作在动机、实现和评估协议方面往往存在显著差异，而松散定义的记忆术语进一步模糊了概念的清晰度。传统的分类方法，如长/短期记忆，证明不足以捕捉当代代理记忆系统的多样性。 本文旨在提供当前代理记忆研究的最新全景图。我们首先明确界定代理记忆的范围，并将其与诸如大型语言模型（LLM）记忆、检索增强生成（RAG）、上下文工程等相关概念区分开来。然后，我们通过形式、功能和动态性这三大统一视角审视代理记忆。 从形式的角度来看，我们识别出三种主导型的代理记忆实现方式：令牌级、参数化和潜在记忆。从功能角度来看，我们提出了一种更细粒度的分类法，区分事实记忆、体验记忆和工作记忆。从动态性的角度来看，我们分析了如何随着时间推移形成、演变和检索记忆。 为了支持实际开发，我们编制了一份全面的记忆基准测试和开源框架汇总表。超越整合之外，我们还提出了对未来研究前沿的前瞻性视角，包括记忆自动化、强化学习集成、多模态记忆、多代理记忆以及可信性问题。我们希望此次调查不仅作为现有工作的参考，还可以作为重新思考未来智能设计中记忆这一首要原始概念的概念基础。
URL
https://arxiv.org/abs/2512.13564
